Integrating gene expression, genomic, and phosphoproteomic data to infer transcription factor activity in lung cancer.

Transcription factors (TFs) are key regulators of cellular gene expression programs in health and disease. Here we set out to integrate genomic, transcriptomic, and phosphoproteomic data to characterize TF activity in lung adenocarcinoma patients. Using expression data from patient samples and genomic information on TF binding to super-enhancers, starting from a list of 1667 human TFs we calculated a patient-specific activity score and identified 34 with perturbed activity in the cancer samples, as evidenced by the expression of their direct targets.

Human lncRNAs harbor conserved modules embedded in different sequence contexts.

We analyzed the structure of human long non-coding RNA (lncRNAs) genes to investigate whether the non-coding transcriptome is organized in modular domains, as is the case for protein-coding genes. To this aim, we compared all known human lncRNA exons and identified 340 pairs of exons with high sequence and/or secondary structure similarity but embedded in a dissimilar sequence context. We grouped these pairs in 106 clusters based on their reciprocal similarities.

A Meta-Analysis Approach to Gene Regulatory Network Inference Identifies Key Regulators of Cardiovascular Diseases.

Cardiovascular diseases (CVDs) represent a major concern for global health, whose mechanistic understanding is complicated by a complex interplay between genetic predisposition and environmental factors. Specifically, heart failure (HF), encompassing dilated cardiomyopathy (DC), ischemic cardiomyopathy (ICM), and hypertrophic cardiomyopathy (HCM), is a topic of substantial interest in basic and clinical research. Here, we used a Partial Correlation Coefficient-based algorithm (PCC) within the context of a meta-analysis framework to construct a Gene Regulatory Network (GRN) that identifies key regulators whose activity is perturbed in Heart Failure.

Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction.

Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, and patient-specific genomic and transcriptomic data with a prior knowledge signaling network using a Boolean-based formalism.

Large-scale DNA sequencing identifies rare variants associated with Systemic Lupus Erythematosus susceptibility in known risk genes.

The identification of rare genetic variants associated to Systemic Lupus Erythematosus (SLE) could also help to understand the pathogenic mechanisms at the basis of the disease. In this study we have analyzed a cohort of 200 Italian SLE patients in order to explore the rare protein-coding variants in five genes (TNFAIP3, STAT4, IL10, TRAF3IP2, and HCP5) already investigated for commons variants found associated in our previous studies. Genomic DNA of 200 SLE patients was sequenced by whole exome sequencing.

Clinical and functional characterization of p.Gly444Ser variant: From a fetal phenotype to a previously undisclosed postnatal phenotype.

gene encodes the alpha-1 chain of type-II procollagen. Heterozygous pathogenic variants are associated with the broad clinical spectrum of genetic diseases known as type-II collagenopathies. We aimed to characterize the NM_001844.

Inhibition of exosome biogenesis affects cell motility in heterogeneous sub-populations of paediatric-type diffuse high-grade gliomas.

Background: Paediatric-type diffuse High-Grade Gliomas (PDHGG) are highly heterogeneous tumours which include distinct cell sub-populations co-existing within the same tumour mass. We have previously shown that primary patient-derived and optical barcoded single-cell-derived clones function as interconnected networks. Here, we investigated the role of exosomes as a route for inter-clonal communication mediating PDHGG migration and invasion.

NRF2 connects Src tyrosine kinase to ferroptosis resistance in glioblastoma.

Glioblastoma is a severe brain tumor characterized by an extremely poor survival rate of patients. Glioblastoma cancer cells escape to standard therapeutic protocols consisting of a combination of ionizing radiation and temozolomide alkylating drugs that trigger DNA damage by rewiring of signaling pathways. In recent years, the up-regulation of factors that counteract ferroptosis has been highlighted as a major driver of cancer resistance to ionizing radiation, although the molecular connection between the activation of oncogenic signaling and the modulation of ferroptosis has not been clarified yet.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs.

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs.

Looking for Plant microRNAs in Human Blood Samples: Bioinformatics Evidence and Perspectives.

Literature has proposed the existence of a cross kingdom regulation (CRK) between human and plants. In this context, microRNAs present in edible plants would be acquired through diet by the consumer's organism and transported via bloodstream to tissues, where they would modulate gene expression. However, the validity of this phenomenon is strongly debated; indeed, some scholars have discussed both the methodologies and the results obtained in previous works.

Dissecting the Genome for Drug Response Prediction.

The prediction of the cancer cell lines sensitivity to a specific treatment is one of the current challenges in precision medicine. With omics and pharmacogenomics data being available for over 1000 cancer cell lines, several machine learning and deep learning algorithms have been proposed for drug sensitivity prediction. However, deciding which omics data to use and which computational methods can efficiently incorporate data from different sources is the challenge which several research groups are working on.

Variation in the co-expression profile highlights a loss of miRNA-mRNA regulation in multiple cancer types.

Recent research provides insight into the ability of miRNA to regulate various pathways in several cancer types. Despite their involvement in the regulation of the mRNA via targeting the 3'UTR, there are relatively few studies examining the changes in these regulatory mechanisms specific to single cancer types or shared between different cancer types. We analyzed samples where both miRNA and mRNA expression had been measured and performed a thorough correlation analysis on 7494 experimentally validated human miRNA-mRNA target-gene pairs in both healthy and tumoral samples.

BRIO: a web server for RNA sequence and structure motif scan.

The interaction between RNA and RNA-binding proteins (RBPs) has a key role in the regulation of gene expression, in RNA stability, and in many other biological processes. RBPs accomplish these functions by binding target RNA molecules through specific sequence and structure motifs. The identification of these binding motifs is therefore fundamental to improve our knowledge of the cellular processes and how they are regulated.

Inhibition of HECT E3 ligases as potential therapy for COVID-19.

SARS-CoV-2 is responsible for the ongoing world-wide pandemic which has already taken more than two million lives. Effective treatments are urgently needed. The enzymatic activity of the HECT-E3 ligase family members has been implicated in the cell egression phase of deadly RNA viruses such as Ebola through direct interaction of its VP40 Protein.

Modeling cancer drug response through drug-specific informative genes.

Recent advances in pharmacogenomics have generated a wealth of data of different types whose analysis have helped in the identification of signatures of different cellular sensitivity/resistance responses to hundreds of chemical compounds. Among the different data types, gene expression has proven to be the more successful for the inference of drug response in cancer cell lines. Although effective, the whole transcriptome can introduce noise in the predictive models, since specific mechanisms are required for different drugs and these realistically involve only part of the proteins encoded in the genome.